README.md

---
task_categories:
- feature-extraction
pretty_name: HPLT2-embeddings
size_categories:
- n>1T
language:
- sq
- bg
- ca
- cs
- da
- de
- es
- et
- el
- eu
- fi
- fr
- gl
- ga
- hr
- hu
- hy
- is
- it
- lv
- lt
- mk
- nl
- pl
- pt
- ro
- sl
- sk
- sr
- tr
- sv
- nb
- nn
configs:
- config_name: als_Latn
  data_files:
  - split: train
    path: als_Latn/*
- config_name: bul_Cyrl
  data_files:
  - split: train
    path: bul_Cyrl/*
- config_name: cat_Latn
  data_files:
  - split: train
    path: cat_Latn/*
- config_name: ces_Latn
  data_files:
  - split: train
    path: ces_Latn/*
- config_name: dan_Latn
  data_files:
  - split: train
    path: dan_Latn/*
- config_name: deu_Latn
  data_files:
  - split: train
    path: deu_Latn/*
- config_name: ekk_Latn
  data_files:
  - split: train
    path: ekk_Latn/*
- config_name: ell_Grek
  data_files:
  - split: train
    path: ell_Grek/*
- config_name: eus_Latn
  data_files:
  - split: train
    path: eus_Latn/*
- config_name: fin_Latn
  data_files:
  - split: train
    path: fin_Latn/*
- config_name: fra_Latn
  data_files:
  - split: train
    path: fra_Latn/*
- config_name: gle_Latn
  data_files:
  - split: train
    path: gle_Latn/*
- config_name: glg_Latn
  data_files:
  - split: train
    path: glg_Latn/*
- config_name: hrv_Latn
  data_files:
  - split: train
    path: hrv_Latn/*
- config_name: hun_Latn
  data_files:
  - split: train
    path: hun_Latn/*
- config_name: hye_Armn
  data_files:
  - split: train
    path: hye_Armn/*
- config_name: isl_Latn
  data_files:
  - split: train
    path: isl_Latn/*
- config_name: ita_Latn
  data_files:
  - split: train
    path: ita_Latn/*
- config_name: lit_Latn
  data_files:
  - split: train
    path: lit_Latn/*
- config_name: lvs_Latn
  data_files:
  - split: train
    path: lvs_Latn/*
- config_name: mkd_Cyrl
  data_files:
  - split: train
    path: mkd_Cyrl/*
- config_name: nld_Latn
  data_files:
  - split: train
    path: nld_Latn/*
- config_name: nno_Latn
  data_files:
  - split: train
    path: nno_Latn/*
- config_name: nob_Latn
  data_files:
  - split: train
    path: nob_Latn/*
- config_name: pol_Latn
  data_files:
  - split: train
    path: pol_Latn/*
- config_name: por_Latn
  data_files:
  - split: train
    path: por_Latn/*
- config_name: ron_Latn
  data_files:
  - split: train
    path: ron_Latn/*
- config_name: slk_Latn
  data_files:
  - split: train
    path: slk_Latn/*
- config_name: slv_Latn
  data_files:
  - split: train
    path: slv_Latn/*
- config_name: spa_Latn
  data_files:
  - split: train
    path: spa_Latn/*
- config_name: srp_Cyrl
  data_files:
  - split: train
    path: srp_Cyrl/*
- config_name: swe_Latn
  data_files:
  - split: train
    path: swe_Latn/*
- config_name: tur_Latn
  data_files:
  - split: train
    path: tur_Latn/*
- config_name: ukr_Cyrl
  data_files:
  - split: train
    path: ukr_Cyrl/*
---
# HPLT2-embeddings

## Dataset summary

HPLT2-embeddings is an extension of the [**HPLT2**](https://hplt-project.org/datasets/v2.0) dataset, annotated with **document-level** [**Snowflake's Arctic-embed-m-v2.0**](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) **embeddings** for **35 languages**, making the dataset **useful for a variety of tasks**, including document clustering, filtering, and other multilingual research.

Snowflake-arctic-embed-m-v2.0 has a sequence length limit of 8192 tokens, each document's embeddings are obtained by using the CLS token to embed each document.

The embeddings were computed as part of our [**🦊 JQL: Judging Quality across Languages**](https://huggingface.co/spaces/JQL-AI/JQL) project and will be the basis for an upcoming high-quality subset of HPLT2. 
We believe that they can be useful for other multilingual research and applications.

For more details, see our paper [Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models](https://arxiv.org/abs/2505.22232).


## Usage

You can load the dataset in Python using e.g.pandas:

```python
import h5py
import pandas as pd

# Path to your .h5 file
file_path = "000_001_00000.h5"  # <-- Replace with your actual file path

# Open the HDF5 file and load data
with h5py.File(file_path, "r") as f:
    # Load the embeddings and document IDs from the "train" group
    embeddings = f["train/embeddings"][:]
    document_ids = f["train/document_id"][:]

# Convert document IDs from bytes (if needed)
if isinstance(document_ids[0], bytes):
    document_ids = [doc_id.decode("utf-8") for doc_id in document_ids]

# Optionally: create a DataFrame (only if embeddings aren't too large for RAM)
df = pd.DataFrame(embeddings)
df.insert(0, "document_id", document_ids)  # Add document_id as the first column

# Preview the DataFrame
print(df.head())
print(f"Loaded {len(df)} rows with shape {embeddings.shape[1]}-dimensional embeddings.")

```




## Origin of the Dataset

This dataset, derived from HPLT2, includes web content collected from 2013 to 2024. As HPLT2 is sourced from the broader internet, it may contain some personally identifiable information (PII), despite efforts to anonymize email addresses and public IP addresses during processing.


## Considerations for Data Usage

For information on social impact, potential biases, and known limitations, please refer to the  [HPLT2 documentation](https://hplt-project.org/datasets/v2.0).


## Citation information
If you use this dataset in your research or applications, please use the following citation:
```
@article{ali2025judging,
    title     = {Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models},
    author    = {
      Mehdi Ali,
      Manuel Brack,
      Max Lübbering,
      Elias Wendt,
      Abbas Goher Khan,
      Richard Rutmann,
      Alex Jude,
      Maurice Kraus,
      Alexander Arno Weber,
      Felix Stollenwerk,
      David Kaczér,
      Florian Mai,
      Lucie Flek,
      Rafet Sifa,
      Nicolas Flores-Herr,
      Joachim Köhler,
      Patrick Schramowski,
      Michael Fromm,
      Kristian Kersting
    },
    year      = {2025},
    journal   = {arXiv preprint arXiv:2505:22232}
  }

```